mirror of https://github.com/coqui-ai/TTS.git
update unit test for extract tts spectrograms script
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Edresson
parent
501c8e0302
commit
e3f56b613b
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@ -5,81 +5,62 @@ import torch
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from tests import get_tests_input_path
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from TTS.tts.models.tacotron2 import Tacotron2
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from TTS.tts.models.glow_tts import GlowTTS
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from tests import get_tests_output_path, run_cli
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from TTS.tts.utils.generic_utils import setup_model
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from TTS.utils.audio import AudioProcessor
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from TTS.utils.io import load_config
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from TTS.bin.extract_tts_spectrograms import inference
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from TTS.tts.utils.text.symbols import phonemes, symbols
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torch.manual_seed(1)
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use_cuda = torch.cuda.is_available()
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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c = load_config(os.path.join(get_tests_input_path(), "test_config.json"))
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# set params from tacotron inference
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c.bidirectional_decoder = False
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c.double_decoder_consistency = False
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ap = AudioProcessor(**c.audio)
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# pylint: disable=protected-access
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class TestExtractTTSSpectrograms(unittest.TestCase):
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@staticmethod
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def test_GlowTTS():
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input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
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input_lengths = torch.randint(100, 129, (8,)).long().to(device)
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input_lengths[-1] = 128
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mel_spec = torch.rand(8, c.audio["num_mels"], 30).to(device)
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mel_lengths = torch.randint(20, 30, (8,)).long().to(device)
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# set paths
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config_path = os.path.join(get_tests_input_path(), "test_glow_tts.json")
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checkpoint_path = os.path.join(get_tests_output_path(), 'checkpoint_test.pth.tar')
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output_path = os.path.join(get_tests_output_path(), 'output_extract_tts_spectrograms/')
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# load config
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c = load_config(config_path)
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# create model
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model = GlowTTS(
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num_chars=32,
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hidden_channels_enc=48,
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hidden_channels_dec=48,
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hidden_channels_dp=32,
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out_channels=c.audio["num_mels"],
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encoder_type="rel_pos_transformer",
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encoder_params={
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"kernel_size": 3,
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"dropout_p": 0.1,
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"num_layers": 6,
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"num_heads": 2,
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"hidden_channels_ffn": 16, # 4 times the hidden_channels
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"input_length": None,
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},
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use_encoder_prenet=True,
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num_flow_blocks_dec=12,
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kernel_size_dec=5,
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dilation_rate=1,
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num_block_layers=4,
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dropout_p_dec=0.0,
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num_speakers=0,
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c_in_channels=0,
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num_splits=4,
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num_squeeze=1,
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sigmoid_scale=False,
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mean_only=False,
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).to(device)
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model.eval()
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_ = inference('glow_tts', model, c, ap, input_dummy, input_lengths, mel_spec.permute(0, 2, 1), mel_lengths)
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print("GlowTTS extract tts spectrograms ok !")
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num_chars = len(phonemes if c.use_phonemes else symbols)
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model = setup_model(num_chars, 1, c, speaker_embedding_dim=None)
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# save model
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torch.save({"model": model.state_dict()}, checkpoint_path)
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# run test
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run_cli(f'CUDA_VISIBLE_DEVICES="" python3 TTS/bin/extract_tts_spectrograms.py --config_path "{config_path}" --checkpoint_path "{checkpoint_path}" --output_path "{output_path}"')
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run_cli(f'rm -rf "{output_path}" "{checkpoint_path}"')
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@staticmethod
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def test_Tacotron2():
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# set paths
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config_path = os.path.join(get_tests_input_path(), "test_tacotron2_config.json")
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checkpoint_path = os.path.join(get_tests_output_path(), 'checkpoint_test.pth.tar')
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output_path = os.path.join(get_tests_output_path(), 'output_extract_tts_spectrograms/')
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# load config
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c = load_config(config_path)
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# create model
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num_chars = len(phonemes if c.use_phonemes else symbols)
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model = setup_model(num_chars, 1, c, speaker_embedding_dim=None)
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# save model
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torch.save({"model": model.state_dict()}, checkpoint_path)
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# run test
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run_cli(f'CUDA_VISIBLE_DEVICES="" python3 TTS/bin/extract_tts_spectrograms.py --config_path "{config_path}" --checkpoint_path "{checkpoint_path}" --output_path "{output_path}"')
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run_cli(f'rm -rf "{output_path}" "{checkpoint_path}"')
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@staticmethod
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def test_Tacotron():
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input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
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input_lengths = torch.randint(100, 128, (8,)).long().to(device)
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input_lengths = torch.sort(input_lengths, descending=True)[0]
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mel_spec = torch.rand(8, 30, c.audio["num_mels"]).to(device)
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mel_lengths = torch.randint(20, 30, (8,)).long().to(device)
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mel_lengths[0] = 30
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# set paths
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config_path = os.path.join(get_tests_input_path(), "test_tacotron_config.json")
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checkpoint_path = os.path.join(get_tests_output_path(), 'checkpoint_test.pth.tar')
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output_path = os.path.join(get_tests_output_path(), 'output_extract_tts_spectrograms/')
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# load config
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c = load_config(config_path)
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# create model
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model = Tacotron2(num_chars=24, decoder_output_dim=c.audio["num_mels"], r=c.r, num_speakers=1).to(device)
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model.eval()
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_ = inference('tacotron2', model, c, ap, input_dummy, input_lengths, mel_spec, mel_lengths)
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print("Tacotron extract tts spectrograms ok !")
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num_chars = len(phonemes if c.use_phonemes else symbols)
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model = setup_model(num_chars, 1, c, speaker_embedding_dim=None)
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# save model
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torch.save({"model": model.state_dict()}, checkpoint_path)
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# run test
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run_cli(f'CUDA_VISIBLE_DEVICES="" python3 TTS/bin/extract_tts_spectrograms.py --config_path "{config_path}" --checkpoint_path "{checkpoint_path}" --output_path "{output_path}"')
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run_cli(f'rm -rf "{output_path}" "{checkpoint_path}"')
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